Cost-Comparison Analysis of a Physician-Delivered Step-Count Prescription Strategy
Bibliographic record
Abstract
BACKGROUND: ial Health (SMARTER) trial demonstrated a physician-delivered step-count prescription strategy to increase steps by more than this amount over 1 year, compared to usual care. In the present analysis, we aimed to determine the costs of the intervention compared to usual care, incorporating 1-year intervention costs and projected savings from lower CVD hospitalizations over the subsequent 5 years. METHODS: We considered Canadians aged 55 to 74 years with type 2 diabetes and/or hypertension. Using time estimates from our trial, we computed nursing costs corresponding to patient support time over 1 year, and pedometer costs for an anticipated 50% of patients without a smartphone. We estimated the number of CVD hospitalizations, the reduction expected with a mean 1000 steps/d increase, and the associated savings. We calculated the net cost (savings), the proportion of patients with their own device required for cost neutrality, and costs (savings) if all patients needed to be provided with a device. RESULTS: At an average intervention cost of $51.28/patient, the total cost would be $168 million. With an estimated 8875 CVD events prevented, $208 million would be saved. This savings would result in ~$40 million in net savings with 50% device ownership, cost neutrality with 25% device ownership, and ~$42 million in net costs if all patients required the healthcare system to provide a device. CONCLUSIONS: At current levels of smartphone ownership, adoption of the SMARTER strategy is cost-saving to cost-neutral from the healthcare system perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".